OFFICIAL NEURAL MOCO RANKINGS ARE EXTRACTOR-CONDITIONED: A FINITE-K PROTOCOL AUDIT

Neural multi-objective combinatorial optimization is used for multi-attribute route planning and resource allocation. Published evaluations report a finite approximate Pareto set extracted by preference-based scalarization: one survivor per preference vector. That official archive is a legitimate score of the advertised preference-to-solution map. The same tables, however, report set-level hypervolume, so a printed ranking mixes per-preference quality with set attainment. We re-score frozen official decoded pools of five open-source constructors (WE-CA, PMOCO, CNH, GIMF-P, and NHDE-P) with a zero-search set-level extractor, Preference-Agnostic Selection (PAS): classical greedy hypervolume subset selection over the entire pool, with no extra neural pass and no local search. PAS strictly exceeds official scalarized extraction on $53$ of $56$ finite-$K$ settings. Mean-rank reversals occur on close pairs with official margins of $4$–$5\times 10^{-4}$; published $0.001$–$0.003$ gaps at larger $n$ need not reverse, because the PAS increment is often similar across a pair. We recommend reporting official and PAS hypervolume in parallel as two legitimate targets. Unmoved-then-Align (U2N) is a post-hoc local refinement on top of PAS. Official finite-$K$ rankings are therefore extractor-conditioned.